Operational verification of a framework for the probabilistic nowcasting of river discharge in small and medium size basins

Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that largescale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in realtime 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h −1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h −1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75-90 % of the forecast errors.

Section: Introductionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

“…1a) at an altitude of 1386 m, that works operationally with a 10 min scansion time and 1 km×1 km spatial resolution. The rainfall field is estimated through the algo-rithm described in Silvestro et al (2009), currently used by the Meteorological Weather Service in the Italian regions of Piedmont and Liguria and by the Italian Civil Protection Department. The observed rainfall field is also the input for the nowcasting model, and it is used in the assimilation scheme, as described in the following section.…”

Section: Study Area and Input Datamentioning

confidence: 99%

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Poletti

Silvestro

Davolio

et al. 2019

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

“…2. Expansion of the limits of the forecasting system: for example the redesign and use of the system for the detection of a wider range of phenomena, such as to capture local flash floods or urban surface water flooding or to provide forecasts in areas where verification is difficult (Brown et al, 2010;Liechti et al, 2013;Silvestro and Rebora, 2012);…”

Section: Areas Of Potential Improvements Of Hepsmentioning

confidence: 99%

HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"

Wetterhall

Pappenberger

Alfieri

et al. 2013

Abstract. Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.